Zaka Ur Rehman | Deep Learning and Medical Imaging | Best Researcher Award

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Dr. Zaka Ur Rehman | Deep Learning and Medical Imaging | Best Researcher Award

Postdoctoral Researcher at Multimedia University, Malaysia

Zaka Ur Rehman is a dedicated AI researcher specializing in digital pathology and biomedical image analysis. Currently based in Cyberjaya, Malaysia, he is pursuing a Ph.D. in Engineering at Multimedia University with a research focus on machine learning, deep learning, and data analysis. His professional journey encompasses teaching, advanced algorithm development, and medical image interpretation. With over five years of academic and industry experience, Zaka has demonstrated a strong commitment to AI research, especially in medical diagnostics. His expertise spans the use of CNNs, Vision Transformers, and self-supervised learning to solve real-world healthcare problems. He is the author of several impactful publications in top-tier journals and has presented his work at esteemed international conferences. Beyond his research contributions, Zaka actively engages in workshops and training sessions to promote scientific communication and technical writing. He is also known for his involvement in various academic collaborations and capacity-building programs. With a cumulative journal impact factor of over 17, his work significantly advances the field of computational pathology. Zaka is fluent in English and Urdu, skilled in programming, and passionate about knowledge dissemination. His dedication and technical acumen make him a valuable contributor to AI-based healthcare innovation.

Profile

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Education

Zaka Ur Rehman’s academic path reflects a progressive journey into specialized domains of engineering and artificial intelligence. He is currently enrolled in a Ph.D. program at Multimedia University, Malaysia (2021–2025), focusing on digital pathology and AI. His CGPA of 3.8/4.0 is a testament to his academic rigor. Prior to this, he completed his M.S. in Electrical Engineering from COMSATS University, Islamabad, Pakistan (2015–2018), with a specialization in biomedical image processing and a CGPA of 3.51/4.0. His master’s thesis centered on brain tumor segmentation using machine learning techniques. Zaka holds a B.Sc. in Computer Systems Engineering from The Islamia University of Bahawalpur, Pakistan (2010–2014), where he explored networks, image processing, and graphics. Earlier academic milestones include his F.Sc. in Pre-Engineering (75%) and Matriculation in Science (78.5%), highlighting consistent excellence in mathematics, physics, and chemistry. His educational foundation is robustly interdisciplinary, bridging computer systems, electrical engineering, and artificial intelligence. With strong theoretical grounding and practical implementation, Zaka’s education has prepared him to tackle complex biomedical challenges through computational means, especially within healthcare imaging. His academic progression aligns seamlessly with his current research on computational histopathology and deep learning, setting a solid stage for his scholarly and professional pursuits.

Professional Experience

Zaka Ur Rehman’s professional background is diverse and rooted in both academia and industry. He currently serves as a Graduate Research Assistant at Multimedia University, Malaysia (2021–2024), where he leads initiatives in AI-driven digital pathology under the Faculty of Engineering. Previously, from 2018 to 2021, he worked as a Lecturer at the University of Lahore, Gujrat Campus. There, he delivered computer science courses, mentored final-year projects, and contributed to curriculum design and quality assurance processes. His earlier roles include working as a PM Youth Internee at Zarai Taraqiati Bank Ltd. (2016–2017), where he was recognized for outstanding performance in IT support, and as an IT Intern at HR Development Secretariat (2015), where he managed web portals and assisted with server administration. Additionally, he has hands-on teaching experience in core courses like machine learning, image processing, and digital logic design. His pedagogical strengths are complemented by practical insights from his industry stints. Throughout his career, Zaka has maintained a balance between instructional responsibilities and applied research. His ability to navigate both technical development and academic instruction positions him uniquely as a researcher-educator with a strong command over emerging technologies in AI and healthcare informatics.

Research Interest

Zaka Ur Rehman’s research interests lie at the intersection of artificial intelligence and biomedical imaging, with a particular emphasis on digital histopathology. His core focus includes the development of AI models for HER2-SISH/IHC analysis and computational biomarker quantification. He is deeply involved in solving complex problems related to nuclei segmentation, stain normalization, and tumor localization. Zaka’s work leverages deep learning architectures such as convolutional neural networks (CNNs), vision transformers, and attention mechanisms. He is also interested in self-supervised learning for applications in computational pathology. Additional focus areas include retinal fundus analysis, optic disk localization, and facial recognition systems. Notably, his research contributions in superpixel-based segmentation and brain tumor detection have been recognized in everal peer-reviewed publications. His passion for merging healthcare with computer vision continues to drive his investigation into AI-based clinical diagnostic tools. Zaka’s innovative research addresses critical gaps in medical image analysis and enhances the potential for AI to assist in disease detection and treatment planning. His scholarly activities reflect a commitment to pushing the boundaries of AI in healthcare, particularly in pathology, where precision and automation are essential for improved patient outcomes.

Research Skills

Zaka Ur Rehman possesses a rich blend of research and technical skills critical for modern AI-driven healthcare innovation. His core competencies include supervised and unsupervised learning, feature extraction, classification algorithms, and biomedical image segmentation. He is proficient in using scientific tools such as TensorFlow, Keras, MIPAV, and LATEX for deep learning model development and documentation. Zaka is well-versed in programming languages like Python and MATLAB, with additional experience in OpenGL for graphical interfaces. His data analysis skills are evidenced by his handling of large-scale datasets—up to 200GB—for histopathological image processing. He has hands-on experience in creating and optimizing CNNs, vision transformers, and attention-based models for medical diagnostics. His research workflow includes data preprocessing, stain normalization, nuclei segmentation, and cancer-region detection from WSIs (Whole Slide Images). Zaka is also adept at technical communication, frequently conducting workshops and training sessions in scientific writing and LaTeX. His ability to link computational tools with clinical problems makes him a versatile researcher. His holistic skill set spans data handling, algorithm development, visualization, and publication—key components for success in AI-based medical research and interdisciplinary collaborations.

Awards and Honors

Zaka Ur Rehman’s scholarly excellence and leadership have been acknowledged through multiple awards and honors. In 2020, he received a prestigious Final Year Project (FYP) Grant Award worth RM 70,000, funded by IGNITE National Technology Fund under Pakistan’s Ministry of IT—a recognition of his innovative research contributions. Earlier in 2013, he was awarded “Best Student of the Semester” for achieving third position in his academic project within the Department of Computer Engineering at Islamia University Bahawalpur. His publication record boasts a cumulative journal impact factor of 17.53 as of 2018, reflecting his commitment to impactful and high-quality research. Zaka has also been invited to present at major international conferences such as NBEC 2023 and ISPACS 2022, underscoring his credibility in academic circles. His role as an instructor in LaTeX workshops, organized by institutions like the University of Lahore and HEC Pakistan, further testifies to his contributions toward community learning. These accolades highlight not only his technical excellence but also his dedication to academic mentorship, innovation, and scientific communication—hallmarks of a rising scholar in the field of AI and biomedical engineering.

Publications

Zaka Ur Rehman has an impressive publication record that underscores his expertise in computational pathology and AI applications in biomedical imaging. His peer-reviewed journal articles have appeared in reputable publications such as Expert Systems with Applications, Medical Hypotheses, Diagnostics, PeerJ Computer Science, and Cancers. His key works include studies on brain tumor segmentation, optic disc analysis, HER2 biomarker quantification, and stain normalization. Notable among them is his 2019 article on superpixel-based brain tumor segmentation and his 2024 work on deep learning-based HER2-SISH histopathology analysis. These publications are methodologically robust and have been widely cited, reflecting the scholarly impact of his research. He also contributed to conference proceedings at major international platforms like NBEC 2023 and ISPACS 2022. His research encompasses both theoretical model development and experimental validation using large histopathological datasets. Zaka’s publication strategy highlights a balanced focus on novelty, clinical relevance, and reproducibility. He collaborates with esteemed academics from Malaysia, Pakistan, and Saudi Arabia, adding to the global relevance of his research. Through consistent publication in high-impact venues, Zaka is steadily advancing the field of medical image computing and AI-driven diagnostics, positioning himself as a promising voice in academic and translational research.

Conclusion

Zaka Ur Rehman exemplifies a new generation of AI researchers dedicated to bridging technology and healthcare. With a strong academic foundation, practical teaching experience, and a focused research agenda, he has built an impactful profile in biomedical image analysis and digital pathology. His contributions to machine learning, particularly in cancer detection and biomarker quantification, stand out in today’s AI-driven medical landscape. He is skilled in cutting-edge tools and methodologies, fluent in technical communication, and actively involved in academic mentorship. The awards and recognitions he has received highlight his innovative thinking and academic excellence. His publications, often tackling clinically relevant problems, demonstrate both technical rigor and practical utility. Zaka’s multidisciplinary expertise and collaborative spirit are key strengths that will continue to fuel his success in academia and beyond. As he advances toward completing his Ph.D., his work holds great promise for transforming clinical diagnostics and healthcare delivery through intelligent systems. Zaka Ur Rehman is not just a researcher, but a visionary contributor whose work contributes meaningfully to the evolving field of computational medicine and AI.

Qin Qin | Digital Image Processing | Best Researcher Award

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Prof. Dr. Qin Qin | Digital Image Processing | Best Researcher Award

Professor at Guilin University of Electronic Technology, China

Professor Qin Qin is a highly accomplished academic and researcher at Guilin University of Electronic Technology, serving as a professor and master’s supervisor in the field of electronic information. She plays a pivotal role in shaping regional scientific strategies as a recognized expert by the science and technology groups of Jiangxi, Hebei, and Guangxi provinces. In addition, she supports industrial innovation through her supervisory work for the Electronic Information Industry Association of Beihai City, Guangxi Province. Known for her expertise in cutting-edge technologies and interdisciplinary applications, she stands out as a thought leader dedicated to pushing the boundaries of research and education.

Profile

Scopus

ORCID

Education

Professor Qin Qin’s academic background is rooted in electronic information engineering. Her education integrated core principles of signal processing, communication systems, and data technologies, which have become foundational to her research focus on image recognition, artificial intelligence, and sensor networks. This rigorous training laid the groundwork for her subsequent achievements as an educator and innovator, allowing her to effectively address complex challenges in both academic and applied technological contexts.

Experience

With an extensive career spanning academic research and technical consultancy, Professor Qin Qin has led more than ten science and technology projects across major national and provincial platforms. These include strategic initiatives sponsored by the Guangxi Science and Technology Department and the Beihai Science and Technology Bureau, reflecting her ability to deliver real-world solutions through applied research. Beyond the lab, she has also driven reforms in education through projects focused on big data and AI-enabled learning environments. Her combined experience in both educational innovation and industry collaboration underlines her role as a bridge between academia and practice.

Research Interest

Professor Qin Qin’s research interests focus on remote sensing, image change detection, semantic segmentation, and AI-based applications in environmental monitoring. Her recent studies address technical challenges in dynamic visual recognition, coastal ecosystem analysis, and AI-driven education systems. A central theme of her work is the design of adaptive, context-aware, and attention-enhanced models for processing complex image data. Her approach often integrates deep learning, multi-scale fusion, and perceptual parsing networks, making her contributions particularly impactful in the fields of geospatial intelligence and smart sensing.

Award

Professor Qin Qin has received significant recognition for her research and educational contributions. She has been honored with a special prize and a second prize for teaching excellence in Guangxi Province. These awards acknowledge her leadership in educational reform and her success in implementing innovative learning models based on artificial intelligence and big data. Her work has also earned attention at national levels, with several of her research projects receiving high-profile funding and collaboration support. She is currently nominated for the Women Research Award and Best Researcher Award, further reflecting her outstanding achievements in the scientific community.

Publication

Professor Qin Qin has published extensively in peer-reviewed journals, contributing cutting-edge research in the domains of remote sensing and artificial intelligence.

  1. Remote Sensing Image Change Detection Based on Dynamic Adaptive Context Attention, Symmetry, 2025-05-20 — addresses high-accuracy visual change detection using context-aware models.

  2. Multi-Scale Feature Fusion Based on Difference Enhancement for Remote Sensing Image Change Detection, Symmetry, 2025-04-12 — explores advanced multi-scale fusion techniques to improve satellite image interpretation.

  3. Efficient Coastal Mangrove Species Recognition Using Multi-Scale Features Enhanced by Multi-Head Attention, Symmetry, 2025-03-19 — introduces novel feature extraction techniques for classifying vegetation in coastal zones.

  4. Construction of Multi-Scale Fusion Attention Unified Perceptual Parsing Networks for Semantic Segmentation of Mangrove Remote Sensing Images, Applied Sciences, 2025-01-20 — develops a perceptual model for ecological image segmentation.

  5. Research on Online Teaching Evaluation Based on CiteSpace, Book Chapter, 2023 — offers a bibliometric analysis approach to evaluating online education trends.

  6. Design of a Short-Wave Impedance Sampling Module Using Wheatstone Bridge, ACM International Conference Proceedings, 2022 — presents hardware solutions for electrical measurement applications.

  7. Medical Image Segmentation Model Based on Triple Gate MultiLayer Perceptron, Scientific Reports, 2022 — proposes an advanced segmentation model applicable to medical diagnostics.

These publications reflect a balance of theoretical depth and real-world applicability, having been cited by multiple researchers in fields ranging from environmental science to computational medicine.

Conclusion

Professor Qin Qin exemplifies the modern academic leader—an educator, researcher, and innovator whose work spans across disciplines to address both local and global challenges. Her contributions to remote sensing image analysis, artificial intelligence applications, and educational system reform have left a lasting mark on her field. With over 30 patents, major funded projects, and influential publications, she is a compelling figure in the global scientific landscape. Her forward-thinking approach and commitment to interdisciplinary research make her an ideal candidate for international recognition through awards that celebrate excellence in data science and innovation.